Search results for: multinomial endogenous switching regression

Authors: Chih-Wei Cheng, Wei-Jen Chan, Yu-Han Huang, Yi-Tsung Lin, Yen-Wei Huang, Min-Cheng Chen, Shou-Zen Chang, G. Chern, Yuan-Chieh Tseng

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How to develop spin-orbit-torque (SOT) devices with the virtues of field-free, perpendicular magnetic anisotropy (PMA), and low switching current is one of the many challenges in spintronics today. We propose a CoFeB/Ta/CoFeB tri-layer antiferromagnetic SOT device that could meet the above requirements. The device’s PMA was developed by adopting CoFeB–MgO interface. The key to the success of this structure is to ensure that (i)changes of the inter-layer coupling(IEC) and CoFeB anisotropy can occur simultaneously; (ii) one of the CoFeB needs to have a slightly tilted moment in the beginning. When sufficient current is given, the SHEreverses the already-tiltedCoFeB, and the other CoFeB can be reversed simultaneously by the IEC with the field-free nature. Adjusting the thickness of Ta can modify the coupling state to reduce the switching current while the field-free nature was preserved. Micromagnetic simulation suggests that the Néel orange peel effect (NOPE) is non-negligible due to interface roughness and coupling effect in the presence of perpendicular anisotropy. Fortunately, the Néel field induced by the NOPE appears to favor the field-free reversal.

Keywords: CoFeB, spin-orbit torque, antiferromagnetic, MRAM, trilayer

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Authors: Hung-Chu Chen, Han Qi, Bauke de Vries

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Energy consumption in the urban environment has attracted numerous researches in recent decades. However, it is comparatively rare to find literary works which investigated 3D spatial analysis of urban energy demand modelling. In order to analyze the spatial correlation between urban morphology and energy demand comprehensively, this paper investigates their relation by using the spatial regression tool. In addition, the spatial regression tool which is applied in this paper is ordinary least squares regression (OLS) and geographically weighted regression (GWR) model. Normalized Difference Built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI), and building volume are explainers of urban morphology, which act as independent variables of Energy-land use (E-L) model. NDBI and NDVI are used as the index to describe five types of land use: urban area (U), open space (O), artificial green area (G), natural green area (V), and water body (W). Accordingly, annual electricity, gas demand and energy demand are dependent variables of the E-L model. Based on the analytical result of E-L model relation, it revealed that energy demand and urban morphology are closely connected and the possible causes and practical use are discussed. Besides, the spatial analysis methods of OLS and GWR are compared.

Keywords: energy demand model, geographically weighted regression, normalized difference built-up index, normalized difference vegetation index, spatial statistics

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Authors: Roengchai Tansuchat, Woraphon Yamaka, Paravee Maneejuk

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From the past recent, the global financial crisis, economic instability, and large fluctuation in agricultural commodity price have led to increased concerns about the volatility transmission among them. The problem is further exacerbated by commodities volatility caused by other commodity price fluctuations, hence the decision on hedging strategy has become both costly and useless. Thus, this paper is conducted to analysis the volatility spillover effect among major agriculture including corn, soybeans, wheat and rice, to help the commodity suppliers hedge their portfolios, and manage the risk and co-volatility of them. We provide a switching regime approach to analyzing the issue of volatility spillovers in different economic conditions, namely upturn and downturn economic. In particular, we investigate relationships and volatility transmissions between these commodities in different economic conditions. We purposed a Copula-based multivariate Markov Switching GARCH model with two regimes that depend on an economic conditions and perform simulation study to check the accuracy of our proposed model. In this study, the correlation term in the cross-hedge ratio is obtained from six copula families – two elliptical copulas (Gaussian and Student-t) and four Archimedean copulas (Clayton, Gumbel, Frank, and Joe). We use one-step maximum likelihood estimation techniques to estimate our models and compare the performance of these copula using Akaike information criterion (AIC) and Bayesian information criteria (BIC). In the application study of agriculture commodities, the weekly data used are conducted from 4 January 2005 to 1 September 2016, covering 612 observations. The empirical results indicate that the volatility spillover effects among cereal futures are different, as response of different economic condition. In addition, the results of hedge effectiveness will also suggest the optimal cross hedge strategies in different economic condition especially upturn and downturn economic.

Keywords: agricultural commodity futures, cereal, cross-hedge, spillover effect, switching regime approach

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Authors: Arun Goel

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The present paper attempts to investigate the prediction of air entrainment rate and aeration efficiency of a free over-fall jets issuing from a triangular sharp crested weir by using regression based modelling. The empirical equations, support vector machine (polynomial and radial basis function) models and the linear regression techniques were applied on the triangular sharp crested weirs relating the air entrainment rate and the aeration efficiency to the input parameters namely drop height, discharge, and vertex angle. It was observed that there exists a good agreement between the measured values and the values obtained using empirical equations, support vector machine (Polynomial and rbf) models, and the linear regression techniques. The test results demonstrated that the SVM based (Poly & rbf) model also provided acceptable prediction of the measured values with reasonable accuracy along with empirical equations and linear regression techniques in modelling the air entrainment rate and the aeration efficiency of a free over-fall jets issuing from triangular sharp crested weir. Further sensitivity analysis has also been performed to study the impact of input parameter on the output in terms of air entrainment rate and aeration efficiency.

Keywords: air entrainment rate, dissolved oxygen, weir, SVM, regression

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Authors: Stefano Iannello, Massimiliano Materazzi

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Fluidized bed reactor technologies are one of the most valuable pathways for thermochemical conversions of biogenic fuels due to their good operating flexibility. Nevertheless, there are still issues related to the mixing and separation of heterogeneous phases during operation with highly volatile feedstocks, including biomass and waste. At high temperatures, the volatile content of the feedstock is released in the form of the so-called endogenous bubbles, which generally exert a “lift” effect on the particle itself by dragging it up to the bed surface. Such phenomenon leads to high release of volatile matter into the freeboard and limited mass and heat transfer with particles of the bed inventory. The aim of this work is to get a better understanding of the behaviour of a single reacting particle in a hot fluidized bed reactor during the devolatilization stage. The analysis has been undertaken at different fluidization regimes and temperatures to closely mirror the operating conditions of waste-to-energy processes. Beechwood and polypropylene particles were used to resemble the biomass and plastic fractions present in waste materials, respectively. The non-invasive X-ray technique was coupled to particle tracking algorithms to characterize the motion of a single feedstock particle during the devolatilization with high resolution. A high-energy X-ray beam passes through the vessel where absorption occurs, depending on the distribution and amount of solids and fluids along the beam path. A high-speed video camera is synchronised to the beam and provides frame-by-frame imaging of the flow patterns of fluids and solids within the fluidized bed up to 72 fps (frames per second). A comprehensive mathematical model has been developed in order to validate the experimental results. Beech wood and polypropylene particles have shown a very different dynamic behaviour during the pyrolysis stage. When the feedstock is fed from the bottom, the plastic material tends to spend more time within the bed than the biomass. This behaviour can be attributed to the presence of the endogenous bubbles, which drag effect is more pronounced during the devolatilization of biomass, resulting in a lower residence time of the particle within the bed. At the typical operating temperatures of thermochemical conversions, the synthetic polymer softens and melts, and the bed particles attach on its outer surface, generating a wet plastic-sand agglomerate. Consequently, this additional layer of sand may hinder the rapid evolution of volatiles in the form of endogenous bubbles, and therefore the establishment of a poor drag effect acting on the feedstock itself. Information about the mixing and segregation of solid feedstock is of prime importance for the design and development of more efficient industrial-scale operations.

Keywords: fluidized bed, pyrolysis, waste feedstock, X-ray

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Authors: Mehmet Alpaslan Köroğlu, Musa Hakan Arslan, Muslu Kazım Körez

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Basic objective of this study is to create a regression analysis method that can estimate the length of a plastic hinge which is an important design parameter, by making use of the outcomes of (lateral load-lateral displacement hysteretic curves) the experimental studies conducted for the reinforced square concrete columns. For this aim, 170 different square reinforced concrete column tests results have been collected from the existing literature. The parameters which are thought affecting the plastic hinge length such as cross-section properties, features of material used, axial loading level, confinement of the column, longitudinal reinforcement bars in the columns etc. have been obtained from these 170 different square reinforced concrete column tests. In the study, when determining the length of plastic hinge, using the experimental test results, a regression analysis have been separately tested and compared with each other. In addition, the outcome of mentioned methods on determination of plastic hinge length of the reinforced concrete columns has been compared to other methods available in the literature.

Keywords: columns, plastic hinge length, regression analysis, reinforced concrete

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Authors: Shahadut Hossain

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Measurement errors in continuous covariates and/or misclassifications in categorical covariates are common in epidemiological studies. Regression analysis ignoring such mismeasurements seriously biases the estimated main and interaction effects of covariates on the outcome of interest. Thus, adjustments for such mismeasurements are necessary. In this research, we propose a Bayesian parametric framework for eliminating deleterious impacts of covariate mismeasurements in logistic regression. The proposed adjustment method is unified and thus can be applied to any generalized linear and non-linear regression models. Furthermore, adjustment for covariate mismeasurements requires validation data usually in the form of either gold standard measurements or replicates of the mismeasured covariates on a subset of the study population. Initial investigation shows that adequacy of such adjustment depends on the sizes of main and validation samples, especially when prevalences of the categorical covariates are low. Thus, we investigate the impact of main and validation sample sizes on the adjusted estimates, and provide a general guideline about these sample sizes based on simulation studies.

Keywords: measurement errors, misclassification, mismeasurement, validation sample, Bayesian adjustment

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Authors: Naveed Ghani

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With the arrival of higher speed communication links and mature application running over the internet, the requirement for reliable, efficient and robust network designs rising day by day. Multi-Protocol Label Switching technology (MPLS) Virtual Private Networks (VPNs) have committed to provide optimal network services. They are gaining popularity in industry day by day. Enterprise customers are moving to service providers that offer MPLS VPNs. The main reason for this shifting is the capability of MPLS VPN to provide built in security features and any-to-any connectivity. MPLS VPNs improved the network performance due to fast label switching as compare to traditional IP Forwarding but traffic classification and policing was still required on per hop basis to enhance the performance of real time traffic which is delay sensitive (particularly voice and video). QoS (Quality of service) is the most important factor to prioritize enterprise networks’ real time traffic such as voice and video. This thesis is focused on the study of QoS parameters (e.g. delay, jitter and MOS (Mean Opinion Score)) for the real time traffic over MPLS VPNs. DiffServ (Differentiated Services) QoS model will be used over MPLS VPN network to get end-to-end service quality.

Keywords: network, MPLS, VPN, DiffServ, MPLS VPN, DiffServ QoS, QoS Model, GNS2

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Authors: M. Ouassaf, S. Belaid

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A series of quinoline derivatives with antimalarial activity were subjected to two-dimensional quantitative structure-activity relationship (2D-QSAR) studies. Three models were implemented using multiple regression linear MLR, a regression partial least squares (PLS), nonlinear regression (MNLR), to see which descriptors are closely related to the activity biologic. We relied on a principal component analysis (PCA). Based on our results, a comparison of the quality of, MLR, PLS, and MNLR models shows that the MNLR (R = 0.914 and R² = 0.835, RCV= 0.853) models have substantially better predictive capability because the MNLR approach gives better results than MLR (R = 0.835 and R² = 0,752, RCV=0.601)), PLS (R = 0.742 and R² = 0.552, RCV=0.550) The model of MNLR gave statistically significant results and showed good stability to data variation in leave-one-out cross-validation. The obtained results suggested that our proposed model MNLR may be useful to predict the biological activity of derivatives of quinoline.

Keywords: antimalarial, quinoline, QSAR, PCA, MLR , MNLR, MLR

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Authors: Xiaomin Zhong, Alexander Pate, Ya-Ting Yang, Ali Fahmi, Darren M. Ashcroft, Ben Goldacre, Brian Mackenna, Amir Mehrkar, Sebastian C. J. Bacon, Jon Massey, Louis Fisher, Peter Inglesby, Kieran Hand, Tjeerd van Staa, Victoria Palin

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Background: This study aimed to predict risks of potentially inappropriate antibiotic type and repeat prescribing and assess changes during COVID-19. Methods: With the approval of NHS England, we used the OpenSAFELY platform to access the TPP SystmOne electronic health record (EHR) system and selected patients prescribed antibiotics from 2019 to 2021. Multinomial logistic regression models predicted the patient’s probability of receiving an inappropriate antibiotic type or repeating the antibiotic course for each common infection. Findings: The population included 9.1 million patients with 29.2 million antibiotic prescriptions. 29.1% of prescriptions were identified as repeat prescribing. Those with same-day incident infection coded in the EHR had considerably lower rates of repeat prescribing (18.0%), and 8.6% had a potentially inappropriate type. No major changes in the rates of repeat antibiotic prescribing during COVID-19 were found. In the ten risk prediction models, good levels of calibration and moderate levels of discrimination were found. Important predictors included age, prior antibiotic prescribing, and region. Patients varied in their predicted risks. For sore throat, the range from 2.5 to 97.5th percentile was 2.7 to 23.5% (inappropriate type) and 6.0 to 27.2% (repeat prescription). For otitis externa, these numbers were 25.9 to 63.9% and 8.5 to 37.1%, respectively. Interpretation: Our study found no evidence of changes in the level of inappropriate or repeat antibiotic prescribing after the start of COVID-19. Repeat antibiotic prescribing was frequent and varied according to regional and patient characteristics. There is a need for treatment guidelines to be developed around antibiotic failure and clinicians provided with individualised patient information.

Keywords: antibiotics, infection, COVID-19 pandemic, antibiotic stewardship, primary care

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Authors: Yan Yu, Wang Yu, Chen Xintong, Liu Yi, Zhang Yanzhong, Wang Yanji, Chen Xingyu, Zhang Miaocheng, Tong Yi

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Neuromorphic computing based on spiking neural networks (SNNs) has emerged as a promising avenue for building the next generation of intelligent computing systems. Owing to its high-density integration, low power, and outstanding nonlinearity, memristors have attracted emerging attention on achieving SNNs. However, fabricating a low-power and robust memristor-based spiking neuron without extra electrical components is still a challenge for brain-inspired systems. In this work, we demonstrate a TiO₂-based threshold switching (TS) memristor to emulate a leaky integrate-and-fire (LIF) neuron without auxiliary circuits, used to realize single layer fully connected (FC) SNNs. Moreover, our TiO₂-based resistive switching (RS) memristors realize spiking-time-dependent-plasticity (STDP), originating from the Ag diffusion-based filamentary mechanism. This work demonstrates that TiO2-based memristors may provide an efficient method to construct hardware neuromorphic computing systems.

Keywords: leaky integrate-and-fire, memristor, spiking neural networks, spiking-time-dependent-plasticity

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Authors: Mikiyas Adugna

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Effort estimation is among the activities carried out in software development processes. An accurate model of estimation leads to project success. The method of agile effort estimation is a complex task because of the dynamic nature of software development. Researchers are still conducting studies on agile effort estimation to enhance prediction accuracy. Due to these reasons, we investigated and proposed a model on LASSO and Elastic Net regression to enhance estimation accuracy. The proposed model has major components: preprocessing, train-test split, training with default parameters, and cross-validation. During the preprocessing phase, the entire dataset is normalized. After normalization, a train-test split is performed on the dataset, setting training at 80% and testing set to 20%. We chose two different phases for training the two algorithms (Elastic Net and LASSO) regression following the train-test-split. In the first phase, the two algorithms are trained using their default parameters and evaluated on the testing data. In the second phase, the grid search technique (the grid is used to search for tuning and select optimum parameters) and 5-fold cross-validation to get the final trained model. Finally, the final trained model is evaluated using the testing set. The experimental work is applied to the agile story point dataset of 21 software projects collected from six firms. The results show that both Elastic Net and LASSO regression outperformed the compared ones. Compared to the proposed algorithms, LASSO regression achieved better predictive performance and has acquired PRED (8%) and PRED (25%) results of 100.0, MMRE of 0.0491, MMER of 0.0551, MdMRE of 0.0593, MdMER of 0.063, and MSE of 0.0007. The result implies LASSO regression algorithm trained model is the most acceptable, and higher estimation performance exists in the literature.

Keywords: agile software development, effort estimation, elastic net regression, LASSO

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Authors: Gaddafi Sani Shehu, Tankut Yalcınoz, Abdullahi Bala Kunya

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Multilevel inverters such as flying capacitor, diode-clamped, and cascaded H-bridge inverters are very popular particularly in medium and high power applications. This paper focuses on a cascaded H-bridge module using a single direct current (DC) source in order to generate an 11-level output voltage. The noble approach reduces the number of switches and gate drivers, in comparison with a conventional method. The anticipated topology produces more accurate result with an isolation transformer at high switching frequency. Different modulation techniques can be used for the multilevel inverter, but this work features modulation techniques known as selective harmonic elimination (SHE).This modulation approach reduces the number of carriers with reduction in Switching Losses, Total Harmonic Distortion (THD), and thereby increasing Power Quality (PQ). Based on the simulation result obtained, it appears SHE has the ability to eliminate selected harmonics by chopping off the fundamental output component. The performance evaluation of the proposed cascaded multilevel inverter is performed using PSIM simulation package and THD of 0.94% is obtained.

Keywords: cascaded H-bridge multilevel inverter, power quality, selective harmonic elimination

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Authors: Christiana Gauci-Sciberras

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The first language (L1) could be used in foreign language teaching and learning as a pedagogical tool to scaffold new knowledge in the target language (TL) upon linguistic knowledge that the learner already has. In a bilingual context, code-switching between the two languages usually occurs in classrooms. One of the reasons for code-switching is because both languages are used for scaffolding new knowledge. This research paper aims to find out why both the L1 (Maltese) and the L2 (English) are used in the classroom of Mandarin Chinese as a foreign language (CFL) in the bilingual context of Malta. This research paper also aims to find out the learners’ perceptions of the use of a bilingual medium of instruction. Two research methods were used to collect qualitative data; semi-structured interviews with adult learners of Mandarin Chinese and lesson observations. These two research methods were used so that the data collected in the interviews would be triangulated with data collected in lesson observations. The L1 (Maltese) is the language of instruction mostly used. The teacher and the learners switch to the L2 (English) or to any other foreign language according to the need at a particular instance during the lesson.

Keywords: Chinese, bilingual, pedagogical purpose of L1 and L2, CFL acquisition

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Authors: Osamu Komori, Shinto Eguchi, Hiroshi Okamura, Momoko Ichinokawa

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The long time-series data on population assessments are essential for global ecosystem assessment because the temporal change of biomass in such a database reflects the status of global ecosystem properly. However, the available assessment data usually have limited sample sizes and the ratio of populations with low abundance of biomass (collapsed) to those with high abundance (non-collapsed) is highly imbalanced. To allow for the imbalance and uncertainty involved in the ecological data, we propose a binary regression model with mixed effects for inferring ecosystem status through an asymmetric logistic model. In the estimation equation, we observe that the weights for the non-collapsed populations are relatively reduced, which in turn puts more importance on the small number of observations of collapsed populations. Moreover, we extend the asymmetric logistic regression model using propensity score to allow for the sample biases observed in the labeled and unlabeled datasets. It robustified the estimation procedure and improved the model fitting.

Keywords: double robust estimation, ecological binary data, mixed effect logistic regression model, propensity score

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Authors: Rene Valdiviezo-Issa

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In this paper, we use Mexico’s Households Income and Expenditures (ENIGH) survey to explain the behaviour that the urban-rural expenditure gap has had since Mexico’s incorporation to the North American Free Trade Agreement (NAFTA) in 1994 and we compare it with the latest available survey, which took place in 2014. We use real trimestral expenditure per capita (RTEPC) as the measure of welfare. We use quantile regressions and a quantile regression decomposition to describe the gap between urban and rural distributions of log RTEPC. We discover that the decrease in the difference between the urban and rural distributions of log RTEPC, or inequality, is motivated because of a deprivation of the urban areas, in very specific characteristics, rather than an improvement of the urban areas. When using the decomposition we observe that the gap is primarily brought about because differences in returns to covariates between the urban and rural areas.

Keywords: quantile regression, urban-rural inequality, inequality in Mexico, income decompositon

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Authors: Y. Kobori, S. Tanaka, N. Tsukiji, N. Takai, H. Kobayashi

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This paper proposes two kinds of new single-inductor dual-output (SIDO) DC-DC switching converters with ripple-based hysteretic control. First SIDO converters of type 1 utilize the triangular signal generated by the CR-circuit connected across the inductor. This triangular signal is used for generating the PWM signal instead of the saw-tooth signal used in the conventional converters. Second SIDO converters of type 2 utilize the triangular signal generated by the CR-circuit connected across the voltage error amplifier. This paper describes circuit topologies, Operation principles, simulation results and experimental results of the proposed SIDO converters. In simulation results of both type of SIDO converters, static output voltage ripples are less than 5mVpp and over/under shoots of the dynamic load regulations for the output current step are less than +/- 10mV. In experimental results of single output converter of type 2, static output voltage ripples are about 20mVpp. Output ripples of SIDO type 1 converter are about 80mVpp.

Keywords: DC-DC converter, switching converter, SIDO converter, hysteretic control, ripple-based control

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Authors: Michele Catalano, Jacopo Di Domenico, Luca Riccetti, Andrea Teglio

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We develop a data-driven agent based model (ABM) for the Italian economy. We calibrate the model for the initial condition and parameters. As a preliminary step, we replicate the Monte-Carlo simulation for the Austrian economy. Then, we evaluate the dynamic properties of the model: the long-run equilibrium and the allocative efficiency in terms of disequilibrium patterns arising in the search and matching process for final goods, capital, intermediate goods, and credit markets. In this perspective, we use a randomized initial condition approach. We perform a robustness analysis perturbing the system for different parameter setups. We explore the empirical properties of the model using a rolling window forecast exercise from 2010 to 2022 to observe the model’s forecasting ability in the wake of the COVID-19 pandemic. We perform an analysis of the properties of the model with a different number of agents, that is, with different scales of the model compared to the real economy. The model generally displays transient dynamics that properly fit macroeconomic data regarding forecasting ability. We stress the model with a large set of shocks, namely interest policy, fiscal policy, and exogenous factors, such as external foreign demand for export. In this way, we can explore the most exposed sectors of the economy. Finally, we modify the technology mix of the various sectors and, consequently, the underlying input-output sectoral interdependence to stress the economy and observe the long-run projections. In this way, we can include in the model the generation of endogenous crisis due to the implied structural change, technological unemployment, and potential lack of aggregate demand creating the condition for cyclical endogenous crises reproduced in this artificial economy.

Keywords: agent-based models, behavioral macro, macroeconomic forecasting, micro data

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Authors: Masaaki Ii, Takashi Saito, Yasuhiko Tabata, Shintaro Nemoto

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Background: Clinical trials of autologous adipose-derived stem cell (AdSC) therapy for ischemic heart diseases (IHD) are now on-going. We have investigated the hypothesis that combination of AdSCs and statin, an agent with pleiotropic effects, could augment the therapeutic effect on myocardial infarction (MI). Methods and Results: Human AdSC functions with different doses of simvastatin-conjugated nanoparticle (STNP) uptake were evaluated by in vitro assays. STNP promoted the migration activity without changing the proliferation activity, and also up-regulated growth factors. Next, MI was induced by LAD ligation in nude mice, and the mice were assigned in the following groups 3 days after MI: 1) PBS (control), 2) NP-AdSCs (50000 cells), 3) STNP, and 4) STNP-AdSCs (50000 cells). Cardiac functional recovery assessed by echocardiography was improved at 4 weeks after surgery in STNP-AdSC group. Masson’s trichrome-stained sections revealed that LV fibrosis length was reduced, and the number of TUNEL-positive cardiomyocytes was less in STNP-AdSC group. Surprisingly, a number of de novo endogenous Nkx-2.5/GATA4 positive immature cardiomyocytes as well as massive vascular formation were observed in outer layer of infarcted myocardium despite of a few recruited/retained transfused STNP-AdSCs 4 weeks after MI in STNP-AdSC group. Finally, massive myocardial regeneration was observed 8 weeks after MI. Conclusions: Intravenously injected small number of statin nanoparticle-loaded hAdSCs exhibited a potent therapeutic effect inducing endogenous cardiac tissue regeneration.

Keywords: statin, drug delivery system, stem cells, cardiac regeneration

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Authors: Muhammad Kamran Asif

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MPLS stands for Multi-Protocol Label Switching. It is the technology which replaces ATM (Asynchronous Transfer Mode) and frame relay. In this paper, we have designed a full fledge small scale MPLS based service provider network core network model, which provides communication services (e.g. voice, video and data) to the customer more efficiently using label switching technique. Using MPLS VPN provides security to the customers which are either on LAN or WAN. It protects its single customer sites from being attacked by any intruder from outside world along with the provision of concept of extension of a private network over an internet. In this paper, we tried to implement a service provider network using minimum available resources i.e. five 3800 series CISCO routers comprises of service provider core, provider edge routers and customer edge routers. The customers on the one end of the network (customer side) is capable of sending any kind of data to the customers at the other end using service provider cloud which is MPLS VPN enabled. We have also done simulation and emulation for the model using GNS3 (Graphical Network Simulator-3) and achieved the real time scenarios. We have also deployed a NMS system which monitors our service provider cloud and generates alarm in case of any intrusion or malfunctioning in the network. Moreover, we have also provided a video help desk facility between customers and service provider cloud to resolve the network issues more effectively.

Keywords: MPLS, VPN, NMS, ATM, asynchronous transfer mode

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Authors: W. M. Ng, O. B. Iskender, L. Simonini, J. M. Gonzalez

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A multitude of factors, including mechanical imperfections of the deployment system and separation instance of satellites from launchers, oftentimes results in highly uncontrolled initial tumbling motion immediately after deployment. In particular, small satellites which are characteristically launched as a piggyback to a large rocket, are generally allocated a large time window to complete detumbling within the early orbit phase. Because of the saturation risk of the actuators, current algorithms are conservative to avoid draining excessive power in the detumbling phase. This work aims to enable time-optimal switching of control modes during the early phase, reducing the time required to transit from launch to sun-pointing mode for power budget conscious satellites. This assumes the usage of B-dot controller for detumbling and PD controller for pointing. Nonlinear Euler's rotation equations are used to represent the attitude dynamics of satellites and Commercial-off-the-shelf (COTS) reaction wheels and magnetorquers are used to perform the manoeuver. Simulation results will be based on a spacecraft attitude simulator and the use case will be for multiple orbits of launch deployment general to Low Earth Orbit (LEO) satellites.

Keywords: attitude control, detumbling, small satellites, spacecraft autonomy, time optimal control

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Authors: Liaquat Ahmad, Muhammad Aslam, Muhammad Azam

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In this article, we propose a control chart based on repetitive group sampling scheme for the location parameter. This charting scheme is based on the regression estimator; an estimator that capitalize the relationship between the variables of interest to provide more sensitive control than the commonly used individual variables. The control limit coefficients have been estimated for different sample sizes for less and highly correlated variables. The monitoring of the production process is constructed by adopting the procedure of the Shewhart’s x-bar control chart. Its performance is verified by the average run length calculations when the shift occurs in the average value of the estimator. It has been observed that the less correlated variables have rapid false alarm rate.

Keywords: average run length, control charts, process shift, regression estimators, repetitive group sampling

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Authors: Gianna Zou

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Propensity score matching (PSM), introduced by Paul R. Rosenbaum and Donald Rubin in 1983, is a popular statistical matching technique which tries to estimate the treatment effects by taking into account covariates that could impact the efficacy of study medication in clinical trials. PSM can be used to reduce the bias due to confounding variables. However, PSM assumes that the response values are normally distributed. In some cases, this assumption may not be held. In this paper, a machine learning method - Bayesian Additive Regression Tree (BART), is used as a more robust method of matching. BART can work well when models are misspecified since it can be used to model heterogeneous treatment effects. Moreover, it has the capability to handle non-linear main effects and multiway interactions. In this research, a BART Matching Method (BMM) is proposed to provide a more reliable matching method over PSM. By comparing the analysis results from PSM and BMM, BMM can perform well and has better prediction capability when the response values are not normally distributed.

Keywords: BART, Bayesian, matching, regression

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Authors: S. K. Ashiquer Rahman

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the paper concentrations on the importance of hourly compensation, emphasizing the significance of the unemployment rate. There are the two most important factors of a nation these are its unemployment rate and hourly compensation. These are not merely statistics but they have profound effects on individual, families, and the economy. They are inversely related to one another. When we consider the unemployment rate that will probably decline as hourly compensations in manufacturing rise. But when we reduced the unemployment rates and increased job prospects could result from higher compensation. That’s why, the increased hourly compensation in the manufacturing sector that could have a favorable effect on job changing issues. Moreover, the relationship between hourly compensation and unemployment is complex and influenced by broader economic factors. In this paper, we use panel data regression models to evaluate the expected link between hourly compensation and unemployment rate in order to determine the effect of hourly compensation on unemployment rate. We estimate the fixed effects model, evaluate the error components, and determine which model (the FEM or ECM) is better by pooling all 60 observations. We then analysis and review the data by comparing 3 several countries (United States, Canada and the United Kingdom) using panel data regression models. Finally, we provide result, analysis and a summary of the extensive research on how the hourly compensation effects on the unemployment rate. Additionally, this paper offers relevant and useful informational to help the government and academic community use an econometrics and social approach to lessen on the effect of the hourly compensation on Unemployment rate to eliminate the problem.

Keywords: hourly compensation, Unemployment rate, panel data regression models, dummy variables, random effects model, fixed effects model, the linear regression model

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Authors: Florin Leon, Silvia Curteanu

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Developing complete mechanistic models for polymerization reactors is not easy, because complex reactions occur simultaneously; there is a large number of kinetic parameters involved and sometimes the chemical and physical phenomena for mixtures involving polymers are poorly understood. To overcome these difficulties, empirical models based on sampled data can be used instead, namely regression methods typical of machine learning field. They have the ability to learn the trends of a process without any knowledge about its particular physical and chemical laws. Therefore, they are useful for modeling complex processes, such as the free radical polymerization of methyl methacrylate achieved in a batch bulk process. The goal is to generate accurate predictions of monomer conversion, numerical average molecular weight and gravimetrical average molecular weight. This process is associated with non-linear gel and glass effects. For this purpose, an adaptive sampling technique is presented, which can select more samples around the regions where the values have a higher variation. Several machine learning methods are used for the modeling and their performance is compared: support vector machines, k-nearest neighbor, k-nearest neighbor and random forest, as well as an original algorithm, large margin nearest neighbor regression. The suggested method provides very good results compared to the other well-known regression algorithms.

Keywords: batch bulk methyl methacrylate polymerization, adaptive sampling, machine learning, large margin nearest neighbor regression

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Authors: Rumana Ali

Abstract:

The presence of nonlinear and power electronic switching devices produce distorted output and harmonics into the system. This paper presents a technique to analyze harmonics using digital series oscilloscope (DSO). In power distribution system further measurements are done by DSO, and the waveforms are analyzed using FFT program. The results of this proposed work are helpful for the investigator to install an appropriate compensating device to mitigate the harmonics, in turn, improve the power quality. This case study is carried out at AIT Chikmagalur. It is done as a starting step towards the improvement of energy efficiency at AIT Chikmagalur, and with an overall aim of reducing the electricity bill with a complete energy audit of the institution. Strategies were put forth to reach the above objective: The following strategies were proposed to be implemented to analyze the power quality in EEE department of the institution. Strategy 1: The power factor has to be measured using the energy meter. Power factor improvement may reduce the voltage drop in lines. This brings the voltages at the socket in the labs closer to the nominal voltage of 230V, and thus power quality improves. Strategy 2: The harmonics at the power inlet has to be measured by means of a DSO. The DSO waveform is analyzed using FFT to know the percentage harmonic up to the 13th harmonics of 50Hz. Reduction in the harmonics in the inlet of the EEE department may reduce line losses and therefore reduces energy bill to the institution.

Keywords: harmonic analysis, energy bill, power quality, electronic switching devices

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Authors: Djamila Meghraoui, Bachir Boudraa, Thouraya Meksen, M.Boudraa

Abstract:

Parkinson's disease is a pathology that involves characteristic perturbations in patients’ voices. This paper describes a proposed method that aims to diagnose persons with Parkinson (PWP) by analyzing on line their voices signals. First, Thresholds signals alterations are determined by the Multi-Dimensional Voice Program (MDVP). Principal Analysis (PCA) is exploited to select the main voice principal componentsthat are significantly affected in a patient. The decision phase is realized by a Mul-tinomial Bayes (MNB) Classifier that categorizes an analyzed voice in one of the two resulting classes: healthy or PWP. The prediction accuracy achieved reaching 98.8% is very promising.

Keywords: Parkinson’s disease recognition, PCA, MDVP, multinomial Naive Bayes

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Authors: Renzo Castillo, George Tsatsaronis

Abstract:

High-temperature ceramic membranes for air separation represents an important option to reduce the significant efficiency drops incurred in state-of-the-art cryogenic air separation for high tonnage oxygen production required in oxyfuel power stations. This study is focused on the thermodynamic analysis of two power plant model designs: the state-of-the-art supercritical 600ᵒC hard coal plant (reference power plant Nordrhein-Westfalen) and the membrane-based oxyfuel concept implemented in this reference plant. In the latter case, the oxygen is separated through a mixed-conducting hollow fiber perovskite membrane unit in the three-end operation mode, which has been simulated under vacuum conditions on the permeate side and at high-pressure conditions on the feed side. The thermodynamic performance of each plant concept is assessed by conventional exergetic analysis, which determines location, magnitude and sources of efficiency losses, and advanced exergetic analysis, where endogenous/exogenous and avoidable/unavoidable parts of exergy destruction are calculated at the component and full process level. These calculations identify thermodynamic interdependencies among components and reveal the real potential for efficiency improvements. The endogenous and exogenous exergy destruction portions are calculated by the decomposition method, a recently developed straightforward methodology, which is suitable for complex power stations with a large number of process components. Lastly, an improvement priority ranking for relevant components, as well as suggested changes in process layouts are presented for both power stations.

Keywords: exergy, carbon capture and storage, ceramic membranes, perovskite, oxyfuel combustion

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Authors: Mosses E. Lufuke, Lorna M. Kamau

Abstract:

This paper attempted to assess whether the size of the regional economic bloc has an impact to the flow of trade to a particular country. Two different sized blocs (COMESA and EAC) and one country (Tanzania) have been used as the point of references. Using the results from of the analyses, the paper also was anticipated to establish whether it was rational for Tanzania to withdraw its membership from COMESA (the larger bloc) to join EAC (the small one). Gravity model has been used to estimate the relationship between the variables, from which the bilateral trade flows between Tanzania and the eighteen member countries of the two blocs (COMESA and EAC) was employed for the time between 2000 and 2013. In the model, the dummy variable for regional bloc (bloc) at which the Tanzania trade partner countries belong are also added to the model to understand which trade bloc exhibit higher trade flow with Tanzania. From the findings, it was noted that over the period of study (2000-2013) Tanzania acknowledged more than 257% of trade volume in EAC than in COMESA. Conclusive, it was noted that the flow of trade is explained by many other variables apart from the size of regional bloc; and that the size by itself offer insufficient evidence in causality relationship. The paper therefore remain neutral on such staggered switching decision since more analyses are required to establish the country’s trade flow, especially when if it had been in multiple membership of COMESA and EAC.

Keywords: economic bloc, flow of trade, size of bloc, switching

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Authors: Lidija R. Jevrić, Sanja O. Podunavac-Kuzmanović, Strahinja Z. Kovačević

Abstract:

This study considers the selection of the most suitable in silico molecular descriptors that could be used for s-triazine pesticides characterization. Suitable descriptors among topological, geometrical and physicochemical are used for quantitative structure-retention relationships (QSRR) model establishment. Established models were obtained using linear regression (LR) and multiple linear regression (MLR) analysis. In this paper, MLR models were established avoiding multicollinearity among the selected molecular descriptors. Statistical quality of established models was evaluated by standard and cross-validation statistical parameters. For detection of similarity or dissimilarity among investigated s-triazine pesticides and their classification, principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used and gave similar grouping. This study is financially supported by COST action TD1305.

Keywords: chemometrics, classification analysis, molecular descriptors, pesticides, regression analysis

Procedia PDF Downloads 395